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Predictability of Late-Season Tropical Cyclone Accumulated Kinetic Energy Around Taiwan Two Months Ahead

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Predictability of Late-Season Tropical Cyclone Accumulated Kinetic Energy Around Taiwan Two Months Ahead INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. (2017) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.5307 Predictability of late-season tropical cyclone accumulated kinetic energy around Taiwan 2 months ahead Mong-Ming Lu,a* Ching-Teng Leea and Bin Wangb,c a Central Weather Bureau, Taipei, Taiwan b Department of Atmospheric Sciences, Atmosphere-Ocean Research Center, University of Hawaii at Manoa, Honolulu, HI, USA c Earth System Modeling Center, Nanjing University of Information Science and Technology, Nanjing, China ABSTRACT: Long-lead seasonal forecast of tropical cyclone (TC) activity is strongly demanded, albeit challenging, for haz- ard prevention and preparedness in the area prone to TCs. This article attempts to present a late-season (September–November, SON) empirical prediction model to predict the accumulative cyclone kinetic energy (ACE) around Taiwan. The predictors are the sea-surface temperatures (SSTs) and sea-level pressure (SLP) anomaly during the preceding spring and summer seasons over tropical Southeast Asia, subtropical western and central North Pacific, and subtropical North Atlantic. Three predic- tion models are established with the lead times of 0, 1, and 2 months. Different types of large-scale influence on the TC activity are found for the above and below-normal-ACE years, respectively. For the above-normal-ACE years, the favourable large-scale condition is warm SSTs over the west Pacific, South China Sea (SCS), and eastern Indian Ocean, and the associ- ated anomalous cyclonic winds and low SLP over the west Pacific marginal seas. The robust precursors are the warm SSTs over mid-latitude west North Pacific and North Atlantic, and the low SLP over Atlantic during the preceding spring season. For the below-normal-ACE years, the favourable large-scale condition is cold SSTs over Indonesian seas and equatorial west Pacific, warm SST and low SLP over equatorial east Pacific, and the anomalous anticyclonic circulation over the SCSandthe Philippine Sea. The robust precursors are the anomalous SST and SLP during the preceding spring season, with the opposite signs to the above-normal-ACE years. The presented models built on the precursor signals are proved able to generate skilful forecast 2 months ahead. The product ACE-SON can be used for seasonal TC activity outlook in a larger area including the coastal region of southeast China and for seasonal rainfall outlook in Taiwan. KEY WORDS tropical cyclone predictability; western North Pacific typhoons; seasonal forecast; empirical prediction model; Southeast Asia climate; Taiwan climate Received 18 May 2017; Revised 4 August 2017; Accepted 8 September 2017 1. Introduction latitude and longitude pixel represented in percentage that is based on the 46-year (1970–2015) total count of Tropical cyclone (TC) is the most influential high-impact the TCs detected at the pixel divided by the sum of the weather system to Taiwan. About 50% of annual total counts of all pixels in domain. The TC affecting Taiwan is rainfall amount and 80% of weather-related losses and defined as the TC centre entered an area with the boundary damage in Taiwan are associated with TC activity. Suffice as Taiwan’s coastline expanded outwards by 300 km on it to say that accurate TC prediction for the time range ∘ from hours to months in advance is of extreme importance a0.1× 0.1 of latitude and longitude mesh (Figure 1; Lu to the safety and wellbeing of the 23 million residents on et al., 2013, hereinafter referred to as LLW). Figures 1(c) Taiwan island. The focus of this study is on investigating and (d) are the density maps of the affecting Taiwan the predictability of long-lead prediction of the TC activity TCs with entire life taken into account. The JJA map around Taiwan. (Figure 1(c)) shows higher TC density over the South Taiwan is located in a region that prone to TC Passage. China Sea (SCS) than the SON (Figure 1(d)) whereas The TC density maps over the western North Pacific the SON map shows higher density over northern Philip- (WNP) shown in Figures 1(a) and (b) for June–August pines than the JJA. The difference between SON and JJA (JJA) and September–November (SON), respectively, densities (Figure 1(e)) is southeast–northwest oriented clearly present that Taiwan is embedded in the area with tails with higher TC density in SON from Pacific to the highest TC density particularly during the JJA season. Philippine Sea. It suggests that although less frequent the The TC density here is calculated at each 2.5∘ by degree SON TCs have higher possibility of travelling a longer distance over the ocean before invading Taiwan, which is higher than that for JJA TCs. In fact, TC damage in SON * Correspondence to: M.-M. Lu, Central Weather Bureau, No. 64, on average is more serious than that in JJA. Gongyuan Road, Taipei 10048, Taiwan. In this study, the predictand is the accumulative cyclone E-mail: [email protected] kinetic energy (ACE) of the affecting Taiwan TCs. Slightly © 2017 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. M.-M. LU et al. (a) (b) 1970–2015 JJA WNP TC track density (%) 1970–2015 SON WNP TC track density (%) 50°N 50°N 40°N 40°N 30°N 30°N 20°N 20°N 10°N 10°N EQ EQ 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E 170°E 180° 170°W 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E 170°E 180° 170°W (c)1970–2015 JJA TW-300km TC track density (%) (d) 1970–2015 SON TW-300km TC track density (%) 50°N 50°N 40°N 40°N 30°N 30°N 20°N 20°N 10°N 10°N EQ EQ 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E 170°E 180° 170°W 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E 170°E 180° 170°W (e) SON and JJA TW-300km TC track density (%) Difference,1970–2015 50°N 40°N 30°N 20°N 10°N EQ 90°E 100°E 110°E 120°E 130°E 140°E 150°E 160°E 170°E 180° 170°W Figure 1. The TC track density map based on 6-h JTWC best track data from 1970 to 2015. The density is calculated in every 2.5 × 2.5∘ grid box as the total TC frequency in a box divided by the total TC frequency all over the entire WNP basin (equator 60∘N, 180–100∘E) accumulated over 3 months such as (a) June–July–August and (b) September–October–November, and the track density based on affecting Taiwan TCs for (c) JJA, (d), SON, and (e) the difference between SON and JJA. The black circle surround Taiwan represents the 300-km boundary when a TC centre moves across the boundary is affecting Taiwan. [Colour figure can be viewed at wileyonlinelibrary.com]. different from the ACE that used to measure the activ- by the ‘invading’ TCs before September. Table 1 shows ity of individual TC and entire TC seasons, particularly, the ACE correlation between different seasons. The corre- the Atlantic tropical seasons (Bell et al., 2000), the ACE lation is very low between two successive seasons when of the present article refers to the sum of the squares there is no overlapped month(s). For the TC peak sea- of the maximum sustained surface wind speed (knots) son (JJAS), an ACE forecast model presented in LLW measured every 6 h when the TC is within the influ- is used at the Central Weather Bureau (CWB) of Tai- ence domain and only the named system counted. Note wan for real-time forecast. However, for the late season that a named system’s intensity must reach at least trop- (SON) there is not yet a forecast model available. The ical storm strength (wind speed ≥34 kt h−1) during the purpose of this study is to build a SON ACE forecast storm’s entire lifetime. The ACE seasonal distribution model for Taiwan area and to identify the sources of the (Figure 2) shows that 61% of the annual ACE contributed predictability. © 2017 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd Int. J. Climatol. (2017) on behalf of the Royal Meteorological Society. PREDICTABILITY OF LATE-SEASON TROPICAL CYCLONE ACE AROUND TAIWAN Taiwan TC ACE monthly distribution (1970–2015) 65 3. Forecast model and skill evaluation 60 30.42% 55 26.40% 3.1. Predictor selection procedure 50 45 As mentioned before, the predictand of the forecast model ) 2 40 19.08% kt 4 35 is the ACE in the 300-km area surrounding Taiwan accu- 30 mulated during the late typhoon season (SON) denoted as 25 ACE (10 11.42% 20 8.86% ACE-SON. The predictors were selected using a two-step 15 correlation analysis procedure. The first step is to cal- 10 2.17% 5 1.17% 0.00% 0.00% 0.00% 0.40% 0.07% culate the correlation maps between ACE-SON and the 0 Jan Feb Mar Apr May JunJulAug Sep Oct Nov Dec large-scale environment variables in the latitudinal belt of Month 40∘S–60∘N.
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